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The Straightforward Case For A Modest Selloff On Wall Street
The best argument for a pullback on Wall Street may be the simplest: There hasn't been one in a while.
With Monday's modest declines, US equities were a little more than 2% from local highs. At the least, stocks are due to fall another 1% to 3%, according to history.
"The S&P sells off by at least 3-5% on average every two to three months," Deutsche Bank's Parag Thatte said, in a positioning and flows update. The last "meaningful" selloff was in March, around the mini-banking crisis, which
I have all SP500 and NDX data from their inception and often perform analysis like these. The problem that I always run up against is what data to include. All data? or only data that are from periods that I “think” are similar to the current one? there will be fewer 2-3% pullbacks when market is moving about 0.5% per day than when it is moving 1% per day. Actually that is just a guess because I haven’t done that analysis.
what I mainly use these types of analysis for is to get me out of FOMO mode. Now is probably not the best time to be going all in on the longs or the shorts. I am much more in profit taking mode from positions from Oct-March. The worst feeling for me is getting drawn into FOMO and then getting immediately slapped down. Somehow I feel better and have better results if the SP moves up 3% from here pulls back 2% and my indicators say to buy, even though I am buying at a higher price. Everyone is different in this regard.
One way to answer your question is to select different data schemes and try them in your model(s). If there is a significant difference in the outcomes of alternative configurations, then the changes you are making are, in fact, critical and must be understood before reaching any conclusions. If the outcome is robust and leaves the problem with outcomes that are not significantly different, then your question is not interesting. I would bet that what data you choose in the related problems you are studying is definitely critical. The more interesting question to me occurs when one is assessing the research of others. As an editorial reviewer I do this regularly. When critical variables are present and a researcher can make changes at will, then there is a definite possibility that a researcher can get any income s/he prefers, especially when the reader does not know what data points were used, what was omitted, and what alternative models were tested. This whole question will be central to growing the usefulness of AI and to protect its reliability.